The Best 10 LLM Evaluation Tools in 2024

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In 2024, large language models (LLMs) are being used more and more in various industries and applications, evaluating the process’s performance in tasks such as natural language processing, content generation, and customer service automation. With the development of LLMs, evaluation tools have also followed this path, evolving to meet the increasing needs for precision, effectiveness, and resilience. In this article, we explore the top 10 tools on the market right now and discuss how they individually help with the LLM evaluation process.

1. Deepchecks

Deepchecks is certainly at the top as one of the most comprehensive evaluation tools. It is known for its user-friendly interface and features. Deepchecks assesses model accuracy and examines biases, robustness, and interpretability. The capability that stands out is its automated testing framework, which uses a systematic approach to check for inconsistencies and vulnerabilities within LLMs. This ensures that the model is reliable before it is used.

Deepchecks is recognized for its easily applicable interface, which makes the evaluations more accessible to end-users even though they are from different levels of technical knowledge. The tool can be integrated into existing development workflows to work with other tools and systems without requiring different setups or specialized knowledge. This ease of use and integration ensures that organizations can implement LLM evaluations without disrupting or changing their current operations.

Additional features of Deepchecks are the scalability and flexibility that stand out, as they cover a wide range of applications for different sizes of organizations. Whether it is a new start-up or a large company, Deepchecks can be adapted to meet the different needs and challenges of using complex data. This ensures that the platform can adapt to the organization, providing effective evaluation and proper monitoring as the organization grows.

2. LLMbench

LLMbench provides reports that help understand model behavior under different conditions, enabling fine-tuning and optimization. One feature that should be highlighted is the ability to simulate various operational conditions to test LLM performance. This process also includes the different sizes and complexity of the input data and tests how the model performs under stress. It provides a realistic image of how a model will perform when deployed in a real environment and ensures no interruptions during operations.

LLMbench offers comparative analyses that allow benchmarking LLM models according to industry standards or different market competitors. This comparative insight is important and necessary for developers aiming to optimize their models or businesses looking to choose the right LLM for their specific needs.

Here, you can see how to use LLMbench to run performance evaluations.

from llmbench import benchmark, report

# Configure your LLM and the type of benchmark
model_config = {
    "model_name": "YourLLM",
    "test_type": "stress-test",
    "data_complexity": "high"

# Running the benchmark
results = benchmark(model_config)

# Generate and view the report
performance_report = report(results)


3. MLflow

An open-source tool called MLflow can be used to manage the whole machine-learning lifecycle. It is recognized for its feature-experiment tracking system. It allows developers to log parameters, code versions, metrics, and artifacts from machine-learning experiments in a centralized repository. The image below is from the open-source version of the MLflow UI:

dor LLMs, every experiment-from initial tests to final deployments-can be systematically tracked and recorded. This not only ensures reproducibility but also makes it easier to compare multiple tests to identify which combinations produce the best performance.

dLflow includes a feature called MLflow Projects, which is essentially a defined framework for packaging machine learning code. It helps share and reproduce code and can define how to run a project via a simple YAML file containing dependencies and entry points. This streamlines the transition from development to production and ensures compatibility and correct alignment of all necessary components.

dLflow Models represents another integral part of the platform that offers a standard format for packaging machine learning models used in downstream tools—whether for prediction in real-time or batch processes. For LLMs, MLflow provides tools to manage a model’s lifecycle, including version control, stage transitions (from staging to production or archiving), and annotations.

4. Arize AI Phoenix

Arize AI Phoenix offers real-time monitoring and troubleshooting of machine learning models. This platform identifies performance degradation, data drift, and model biases.

A feature of Arize AI Phoenix that should be highlighted is its ability to provide a detailed analysis of model performance in different segments. This means it can identify particular domains where the model might not work as intended. This includes understanding particular dialects or circumstances in language processing tasks. In the case of fine-tuning models to provide consistently good performance across all inputs and user interactions, this segmented analysis is considered quite useful.

The platform’s user interface can sort, filter, and search for traces in the interactive troubleshooting experience. You can also see the specifics of every trace to see what happened during the response-generating process.

Arize AI Phoenix

A detailed image of a RAG application trace in Arize AI Phoenix

However, its attention is limited to just three assessment criteria:

  • In QA Correctness – evaluating a question-and-answer setup’s response correctness.
  • Checking if the model produces false or irrelevant information is known as hallucination.
  • Toxicity is the assessment of the material for any offensive or dangerous words.

The Best 10 LLM Evaluation Tools in 2024

  • Reduce Risk
  • Simplify Compliance
  • Gain Visibility
  • Version Comparison

5. DeepEval

DeepEval focuses on detailed assessments to ensure that LLMs perform accurately and ethically. One of DeepEval’s key features is its testing methodology, which is intended to undertake a series of rigorous tests that assess an LLM’s functional performance. DeepEval considers characteristics of model accuracy, reactivity, and scalability while assessing functional performance. This helps determine how well an LLM comprehends and processes various languages and dialects, as well as how it manages massive amounts of inquiries or difficult computing jobs.

DeepEval prioritizes detecting biases and also guarantees fairness in model outputs. It employs algorithms to determine whether an LLM’s comments favor specific demographics or include discriminatory characteristics.

Another key feature of DeepEval is its capacity to recreate real-world circumstances throughout the testing phase. This includes introducing unexpected or unusual inputs to evaluate how the model behaves and how it ensures reliability. Such tests enable the identification of any flaws in the model that may not be evident during regular testing techniques.

G-Eval is a framework with a chain of thoughts for assessing LLM findings according to particular standards. Start a GEval class and specify the evaluation criteria in common language as follows to build a custom measure using LLMs for evaluation:

from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCaseParams

correctness_metric = GEval(
    criteria="Determine whether the actual output is factually correct based on the expected output.",
    # NOTE: you can only provide either criteria or evaluation_steps, and not both
        "Check whether the facts in 'actual output' contradicts any facts in 'expected output'",
        "You should also heavily penalize omission of detail",
        "Vague language, or contradicting OPINIONS, are OK"
    evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT],

6. RAGAs

RAGAs, short for retrieval-augmented generation assessments, represent a tool designed to evaluate and improve the performance of retrieval-augmented generation models. The primary function of RAGAs is to assess how effectively a model can utilize external data sources during the generation process. RAGAs simulate various scenarios to test the model’s ability to access the correct information from datasets and to integrate this information seamlessly into coherent and contextually appropriate responses.

RAGAs strongly emphasize two main aspects: accuracy and relevance. They evaluate accuracy by checking if the information retrieved and used by the model is correct and truthful. Relevance is assessed by determining whether the information is appropriate for the context of the query. These evaluations help ensure that the model not only provides factually correct answers but also understands the user’s intent and the nuances of the questions posed.

RAGAs implement the continuous improvement approach with iterative testing of models. While developers refine the retrieval mechanisms, RAGAs enable the re-evaluation of the model to track improvements. It also identifies new areas that may require further attention. Here is an example of how to use RAGAs to evaluate a model’s performance:

from ragas import evaluate
from datasets import Dataset, load_dataset

# Prepare your dataset in the specified format
data = load_dataset('your_dataset_name', split='test')

# Define the fields that RAGAs will evaluate
evaluation_fields = {
    'question': 'query_text',
    'contexts': 'retrieved_contexts',
    'answer': 'generated_answer',
    'ground_truth': 'reference_answer'

# Run the evaluation using RAGAs
results = evaluate(data, fields=evaluation_fields)

print("Evaluation Results:", results)

7. ChainForge

ChainForge is a prompt engineering open-source environment that lets users assess the robustness of text-generating models and prompts. With ChainForge, multiple LLMs may be quickly prompted, their answers compared, and theories concerning them tested. Furthermore, it facilitates the verification of the output quality of software developed via LLM calls. The influence of various system messages on ChatGPT output may be measured, output formats can be guaranteed to be consistent, and robustness to trigger injection attacks may be tested.

The platform provides functions such as sending parametrized prompts, caching and exporting them to Excel files without writing code, and confirming response quality for the same model at various settings. You can also run example assessments made with OpenAI’s evaluation tools. The pre-written tests in these evaluations enable you to evaluate the robustness and performance of text-generating models. Using this function helps you avoid starting from scratch when evaluating models.

ChainForge is available as a web version and can also be installed locally for additional features like loading API keys from environment variables, writing Python code to evaluate LLM responses, or querying locally run models. The figure below shows the ChainForge interface for evaluating a model’s robustness to prompt injection attacks:

8. Guardrails AI

At its core, Guardrails AI is customized to enforce ethical compliance and safety standards, and it stays updated with the latest regulatory and ethical guidelines relevant to AI deployments. It proactively updates its monitoring and evaluation criteria based on these guidelines, helping organizations remain in compliance with current and emerging laws and ethical standards.

Guardrails AI represents a Python framework designed to improve the reliability of AI applications, primarily by validating and mitigating risks in the input and output data of LLMs.

Guardrails AI employs input/output guards to detect and mitigate risks in real time. These guards use validators from the Guardrails Hub to evaluate the data against specific criteria, such as profanity, toxicity, or compliance with formatting rules. Then, the ‘parse’ method allows users to validate outputs post-processing. This method can apply RAIL (risk-aware interception language) specifications to the LLM output to ensure the predefined rules. Guardrails AI can also re-ask the LLM if the initial output fails validation. This iterative process improves the final output’s accuracy and reliability.

It is important to highlight that Guardrails AI supports streaming validation, which allows for the real-time assessment of LLM responses as they are generated. This is used for applications that require immediate feedback and corrections.

9. OpenPipe

OpenPipe represents a streamlined platform designed to help product-focused teams train specialized LLM models as replacements for slow and expensive prompts. OpenPipe offers several features that focus on evaluating and improving model performance. The Unified SDK allows teams to collect and use interaction data to fine-tune a custom model, continually refining and improving its performance. OpenPipe Data Capture captures every request and response and stores it for future use. Then, the Request Logs feature automatically logs past requests and tags them for easy filtering. Fine-tuning and pruning rules enable the removal of large chunks of unchanging text and fine-tuning a model on the compacted data, reducing the size of incoming requests and saving resources on inference. The image below illustrates a dataset used for PII redaction within the OpenPipe platform. It shows the detailed logs of dataset entries, including import times and input and output tokens.

To save money on inference, OpenPipe uses pruning rules to remove large chunks of unchanging text and fine-tune a model on the compacted data. After training your model, OpenPipe will automatically host it. It also improves performance and reduces costs by caching previously generated responses. OpenPipe enables you to evaluate your models by comparing them to both OpenAI base models and each other. To ensure your LLMs are productive and successful, you may quickly gain insights into your models’ performance and set up custom instructions.

10. Prompt Flow

Prompt Flow is a Microsoft application that manages and creates efficient prompts while optimizing and assessing how users interact with LLMs. Evaluation and improvement of the structure and quality of input prompts make it possible to fully use LLMs to provide precise and relevant outputs. Prompt Flow improves the questions sent into LLMs to get the most precise and suitable answers. Using a sequence of testing and feedback loops, Prompt Flow determines the optimal structures, evaluates the efficacy of various prompt variations, and makes recommendations for improvement. Prompt engineering represents a procedure that normally needs a lot of manual labor and experience; Prompt Flow automates it. This automation makes the creation and refinement of prompts easier. Real-time feedback and adaptability represent the key features of this tool, which provide insights on quick performance and enable dynamic changes depending on evaluation findings.

The figure below shows the setup and visualization of a directed acyclic graph flow using the Prompt flow extension for Visual Studio Code. It shows how LLM applications can be managed through a YAML file, enabling a code-centric and UI-friendly development approach.

Final tips

To choose a proper evaluation tool, careful consideration of both the tool’s scalability and your specific needs is required. Therefore, finding a solution that works well with your current processes can save you time and money, whether you work for a big firm or a start-up. It is important to verify that your selected platform can grow with your business, providing regular upgrades and improvements to stay current with industry development.

With its user-friendly approach, Deepchecks is the ideal option for LLM assessment. Before deployment, the system’s automated testing framework methodically looks for vulnerabilities and inconsistencies to guarantee model reliability. Because Deepchecks integrates so well with current processes, it is easily applied by people with different technical levels. It is important to highlight that Deepchecks provides a complete solution for LLM evaluation with features covering model correctness, biases, and interpretability.

However, putting all these technologies into place alone is not sufficient. To keep ahead of developing innovative solutions and new industry standards, organizations must use these evaluation platforms regularly. The methods we use to assess and improve LLMs must also change as they do.


The Best 10 LLM Evaluation Tools in 2024

  • Reduce Risk
  • Simplify Compliance
  • Gain Visibility
  • Version Comparison